Method and apparatus for non-destructive target cleanliness characterization by types of flaws sorted by size and location

ABSTRACT

A preferred, non-destructive method for characterizing sputter target cleanliness includes the steps of sequentially irradiating the test sample with sonic energy predominantly of target sputter track areas; detecting echoes induced by the sonic energy; and discriminating texture-related backscattering noise from the echoes to obtain modified amplitude signals. These modified amplitude signals are compared with one or more calibration values so as to detect flaw data points at certain positions or locations where the comparison indicates the presence of at least one flaw. Most preferably, groups of the flaw data pixels corresponding to single large flaws are bound together so as to generate an adjusted set of flaw data points in which each group is replaced with a single, most significant data point. The adjusted set of flaw data point is used to calculate one or more cleanliness factors, or to plot a histogram, which characterizes the cleanliness of the sample.

FIELD OF THE INVENTION

[0001] This invention relates to non-destructive methods and apparatifor detection of types of minute flaws which may be present in sputtertargets and, more particularly, to non-destructive methods and apparatifor target volumetric cleanliness characterization by types of flawssorted by size and location.

BACKGROUND OF THE INVENTION

[0002] Cathodic sputtering is widely used for depositing thin layers orfilms of materials from sputter targets onto desired substrates such assemiconductor wafers. Basically, a cathode assembly including a sputtertarget is placed together with an anode in a chamber filled with aninert gas, preferably argon. The desired substrate is positioned in thechamber near the anode with a receiving surface oriented normally to apath between the cathode assembly and the anode. A high voltage electricfield is applied across the cathode assembly and the anode.

[0003] Electrons ejected from the cathode assembly ionize the inert gas.The electrical field then propels positively charged ions of the inertgas against a sputtering surface of the sputter target. Materialdislodged from the sputter target by the ion bombardment traverses thechamber and deposits on the receiving surface of the substrate to formthe thin layer or film.

[0004] One factor affecting the quality of the layer or film produced bya sputtering process is the “cleanliness” of the material from which thesputter target is made. Since the cleanliness of the material from whicha sputter target is made affects the quality of layers or films producedusing that target, it is obviously desirable to use relatively cleanmaterials in fabricating sputter targets.

[0005] However, since the target material consumption during thesputtering process is highly non-uniform (this is especially true formodern sputter-deposition tools with planar magnetron sources using aspecifically-confined non-uniform magnetic fields formed by rotating orstationary magnets), localized highly eroded regions or “sputter tracks”typically form on the surface of the target. Although the sputter trackregion of the target is heavily eroded, other regions of the targetexperience significantly less erosion or even remain practicallyunsputtered. Due to this difference in erosion intensity, there is asubstantial difference in the contribution of different target regionsto the sputtering process. While the material cleanliness of theheavily-eroded sputter track region is absolutely critical for the filmquality, the cleanliness of the regions where insignificant sputteringor no sputtering occurs is less critical.

[0006] As presently understood, prior art cleanliness criteria did notdistinguish the “sputter track” and “non-sputter track” regions of thetarget surface. Thus, prior art techniques for characterizing sputtertargets did not take into account the possibility that, in a field wherea few or even one single minute flaw can impact on a decision to acceptor reject a target, identifing flaw size and determining whether flawsare located inside or outside the sputter track regions can improvetarget production yield without compromising the functional targetquality.

[0007] For example, FIG. 1 illustrates a technique for characterizingaluminum and aluminum alloy sputter targets similar to the methodsproposed in Leroy et al. U.S. Pat. Nos. 5,887,481 and 5,955,673. Thetechnique illustrated in FIG. 1 employs a pulse-echo method performed ona test sample 10 having a planar upper surface 12 and a parallel planarlower surface 14. In accordance with this technique, focused ultrasonictransducer 16 irradiates each of a sequence of positions on the uppersurface 12 of the test sample 10 with a single, short-duration,high-frequency ultrasound pulse 18 having a frequency of at least 5 MHz,and preferably 10-50 MHz. The ultrasonic transducer 16 then switches toa sensing mode and detects a series of echoes 20 induced by theultrasound pulse 18.

[0008] One factor which contributes to these echoes 20 is scattering ofsonic energy from the ultrasound pulse 18 by flaws 22 in the test sample10. By comparing the amplitudes of echoes 20 induced in the test sample10 with the amplitudes of echoes induced in reference samples (notshown) having compositions similar to that of the test sample 10 andblind, flat-bottomed holes of fixed depth and diameter, it is possibleto detect and count flaws 22 in the test sample 10.

[0009] The number of flaws detected by the technique of FIG. 1 has to benormalized in order to facilitate comparison between test samples ofdifferent size and geometry. Conventionally, the number of flaws isnormalized by volume, that is, the sputter target materials arecharacterized in units of “flaws per cubic centimeter.” The volumeassociated with the echoes 20 from each irradiation of the test sample10 is determined, in part, by estimating an effective cross-section ofthe pulse 18 in the test sample 10.

[0010] A number of factors detract from the ability of the transducer 16to detect sonic energy scattered by the flaws 22. This reduces thesensitivity of the technique.

[0011] One such factor is relative weakness of the scattered energy. Aportion of the scattered energy is attenuated by the material making upthe test sample 10. Furthermore, since the flaw sizes of interest, whichrange from approximately 0.04 mm to 1 mm, are of same order or less thanthe wavelength of ultrasound in metals (for example, the wavelength ofsound in aluminum for the frequency range of 10 MHz is 0.6 mm), thepulse 18 has a tendency to refract around the flaws 22, which reducesthe scattering intensity.

[0012] Another factor detracting from the ability of the transducer 16to detect the sonic energy scattered by the flaws 22 is the noisegenerated by scattering of the pulse 18 at the boundaries between grainshaving different textures. In fact, the texture-related noise can be sogreat for high-purity aluminum having grain sizes on the order ofseveral millimeters that small flaws within a size range ofapproximately 0.05 mm to 0.4 mm and less cannot be detected. Largergrain sizes reduce the signal-to-noise ratio for the sonic energyscattered by the flaws when compared to the noise induced by the grainboundaries.

[0013] Other factors affecting the sensitivity and resolution of thetechnique of FIG. 1 includes the pulse frequency, duration and waveform;the degree of beam focus and the focal spot size; the couplingconditions, that is, the efficiency with which the sonic energy travelsfrom the transducer 16 to the test sample 10; and the data acquisitionsystem parameters.

[0014] Another drawback to the technique of FIG. 1 is that thecalculation of the “flaws per cubic centimeter” in the test sample 10presupposes that only flaws 22 within a determinable cross-sectionalarea scatter sonic energy back toward the transducer 16. In fact, thepulse 18, due to its wave nature, does not have localized,well-determined boundaries.

[0015] The distribution of the energy of the pulse 18 within the testsample 10, under simplifying assumptions, permits one to define acorridor 30 having a determinable cross-section beneath the transducer16 in which most of the energy should be concentrated. Nevertheless,some of the energy of the pulse 18 will propagate outside this corridor30. As a result, the transducer may detect sonic energy scattered byrelatively large flaws 22 located outside the estimated corridor 30,thereby overestimating the density of flaws 22 in the test sample 10 andunderestimating their sizes. Because of this, material cleanlinesscharacteristics become to some degree uncertain.

[0016] Another drawback to the technique of FIG. 1 is inability todetermine the proximity of a flaw to the sputter track region. Thisincreases the risk that a manufacturer will accept targets which areundesirable because of defects located at or near the sputter trackregions. Alternatively, it increases the risk that the manufacturer willreject potentially useful targets to compensate for the risk ofaccepting targets having unacceptable defects in or near the sputtertrack region.

[0017] Thus, there remains a need in the art for non-destructivetechniques for characterizing sputter target materials having greatersensitivity than the method illustrated in FIG. 1. There also remains aneed for techniques which permit the comparison of the cleanliness ofdifferent sputter target materials in a manner which is not dependent onarbitrary volumetric estimations in the form of “flaws per cubic unit.”

[0018] Partially, these drawbacks and limitations have been overcome bythe prior art technique suggested in Tosoh SMD International ApplicationNo. PCT/US99/13066. Application PCT/US99/13066 discloses a method whichovercomes most, though not necessarily all, of the disadvantages statedabove. Since the data collection, analysis and imaging techniquesproposed in Application PCT/US99/13066 are intended to detect, identify,and count flaws with sizes in the range of 0.04 mm to 0.1 mm (that is,flaws having relative sizes less than the size of the single pixel ofthe data acquisition and displaying device), each single flaw isrepresented by the single data point (pixel) with a value equal to thesignal amplitude.

[0019] The technique according to Application No. PCT/US99/13066 countsthe total number of flaw data points or pixels “C_(F)” to quantify thedegree of target material cleanliness. FIG. 2 illustrates this methodfor characterizing the cleanliness of sputter target material. Inaccordance with this method, a cylindrical sample 50 of the sputtertarget material is compressed or worked to produce a disc-shaped testsample 52 having a planar upper surface 54 and a substantially parallelplanar lower surface 56. Thereafter, a focused ultrasonic transducer 60is positioned near the upper surface 54. The transducer 60 irradiatesthe upper surface 54 of the test sample 52 with a single,short-duration, megahertz-frequency-range ultrasonic pulse 62. Thetransducer 60 subsequently detects an echo 64 induced in the test sample52 by the pulse 62. The transducer 60 converts the echo 64 into anelectrical signal (not shown), which is processed for use incharacterizing the test sample 52.

[0020] As illustrated in FIG. 3, the test sample 52 first is immersed indeionized water (not shown) in a conventional immersion tank 80. Thetransducer 60 is mounted on a mechanical X-Y scanner 82 in electricalcommunication with a controller 84, such as a PC controller. Thecontroller 84 is programmed in a conventional manner to induce themechanical X-Y scanning unit 82 to move the transducer 60 in araster-like stepwise motion across the upper surface 54 of the testsample 52.

[0021] The transducer 60 is oriented so that the pulse 62 propagatesthrough the deionized water (not shown) in the immersion tank 80 andstrikes the test sample 52 approximately normally to the upper surface54. The transducer 60 preferably is spaced from the upper surface 54such that the pulse 62 is focused on a zone 86 (FIG. 2) of the testsample 52.

[0022] An echo acquisition system for use in the method of FIGS. 2 and 3includes a low noise gated preamplifier 90 and a low noise linearamplifier 92 with a set of calibrated attenuators. When sufficient timehas elapsed for the echoes to arrive at the transducer 60, thecontroller 84 switches the transducer 60 from a transmitting mode to agated electronic receiving mode. The echoes 64 are received by thetransducer 60 and converted into an RF electric amplitude signal (notshown). The amplitude signal is amplified by the preamplifier 90 and bythe low noise linear amplifier 92 to produce a modified amplitudesignal. The attenuators (not shown) associated with the low noise linearamplifier 92 attenuate a portion of the texture-related noise. Themodified amplitude signal then is digitized by the analog-to-digitalconverter 94 before moving on to the controller 84. Theanalog-to-digital conversion is performed so as to preserve amplitudeinformation from the analog modified amplitude signal.

[0023] Flaws of given sizes are detected by comparing the digitizedmodified amplitude signals obtained from the sample 52 with referencevalues derived from tests conducted on reference samples (not shown)having compositions similar to that of the test sample 52 and blind,flat-bottomed holes of fixed depth and diameter.

[0024] The PC controller 84 includes a microprocessor 100 programmed tocontrol the data acquisition process. The microprocessor 100 also isprogrammed to calculate the cleanliness factor characterizing thematerial of the samples 50, 52. It discriminates the texture-relatedbackscattering noise and distinguishes “flaw data points,” that is, datapoints where comparison of the digitized, modified amplitude signalswith the reference values indicate the presence of flaws. Themicroprocessor 100 maintains a count of the flaw data points detectedduring the testing of a test sample 52 to determine the “flaw count”C_(F). The microprocessor 100 also is programmed to distinguish “no-flawdata points,” that is, data points where comparison of the digitized,modified amplitude signals with the calibration values indicates theabsence of flaws.

[0025] The microprocessor 100 also determines a total number of datapoints “C_(DP,)” that is, the sum of the flaw count C_(F) and the numberof no-flaw data points. Although the total number of data points couldbe determined by adding the counts of the flaw data points and theno-flaw data points, it preferably is determined by counting the totalnumber of positions “C₁” along the upper surface 54 at which the testsample 52 is irradiated by the transducer 60 and subtracting the numberof digitized RF signals “C_(N)” which the data acquisition circuitry wasunable, due to noise or other causes, to identify as either flaw datapoints or no-flaw data points. Having determined the flaw count C_(F)and the total number of data points C_(DP), the microprocessor isprogrammed to calculate a cleanliness factor F_(C)=(C_(F)/C_(DP))×10⁶ tocharacterize the material comprising the samples 50, 52.

[0026] Another way in which the method of FIG. 2 characterizes thematerial comprising the samples 50, 52 is by determining the sizedistribution of flaws in the test sample 52. More specifically, themethod characterizes the cleanliness of the sample 52 by definingamplitude bands or ranges; comparing the amplitudes of the digitized,modified amplitude signals with the amplitude bands so as to formsubsets of the modified amplitude signals; counting the data points inthese subsets of modified amplitude signals to determine a modifiedamplitude signal count for each amplitude band; and constructing ahistogram relating the modified signal counts to said plurality ofamplitude bands. Since the amplitudes represented by the digitized,modified amplitude signals are related to the sizes of flaws detected inthe sample 52, the histogram provides an indication of the flaw sizedistribution in the sample 52.

[0027] However, it has to be taken into consideration that a single flawwith size larger than about 0.1 mm may not be represented by one singledata point. For example, a single flaw larger than about 0.1 mm mayexceed the effective cross-section of a single pulse. This possibilitymakes it more difficult or even impossible to determine the total numberof flaws based on the raw count of flaw data points or pixels.

[0028] Another drawback to the methods of FIGS. 1-3 is that they aredesigned to test and characterize one target or sample (not shown) at atime. That is, the methods as proposed in the references appear to havebeen designed to conduct each test sequentially and without overlap evenwhen a queue of targets or samples (not shown) becomes available fortesting in the course of a manufacturing process.

[0029] Thus, there remains a need in the art for non-destructivetechniques to characterize cleanliness of sputtering target which areable to identify and properly count the flaws with greater range of flawsizes and to provide separate flaw counts for sputter track andnon-sputter track regions.

SUMMARY OF THE INVENTION

[0030] These needs and others are addressed by means of anon-destructive method for characterizing a test sample of a sputtertarget material defining a sputtering surface. A preferred methodincludes the steps of sequentially irradiating the test sample withsonic energy at a plurality of positions on the surface; detectingechoes induced by the sonic energy; and discriminating texture-relatedbackscattering noise from the echoes to obtain modified amplitudesignals. These modified amplitude signals are compared with one or morecalibration values so as to detect flaw data pixels or points at certainpositions or locations where the comparison indicates the presence of atleast one flaw as well as no-flaw data pixels at other positions wherethe comparison indicates an absence of flaws. Most preferably, groups ofthe flaw data pixels corresponding to single large flaws are boundtogether so as to generate an adjusted set of flaw data points in whicheach group of the groups of flaw data pixels is replaced with a single,most significant data point.

[0031] In accordance with one especially preferred embodiment, membersof the adjusted set of flaw data points are counted to determine atleast one flaw count C_(F). A total number of data points C_(DP) isdetermined and a cleanliness factor F_(C)=(C_(F)/C_(DP))×10⁶ iscalculated. This cleanliness factor serves to characterize thecleanliness of the sputter target material.

[0032] In accordance with another especially preferred embodiment, aplurality of amplitude bands are defined. The amplitudes represented bymembers of the adjusted set of flaw data points are compared with theamplitude bands to form a plurality of subsets of the adjusted set offlaw data points, where “n” is the number of the subsets of the adjustedset of flaw data points. Then, members of the subsets of the adjustedset of flaw data points are counted to determine a plurality of flawcounts C_(F1), . . . C_(Fn). The plurality of flaw counts is used toconstruct a histogram relating the flaw counts C_(F1), . . . C_(Fn) tosaid amplitude bands. This histogram then serves to characterize thecleanliness of the sputter target material.

[0033] In accordance with yet another especially preferred embodiment,the flaw data points are classified according to size or location on theupper surface of the test sample. In accordance with one such method,each data point is associated with a position on the surface of the testsample. Only flaw data points associated of the plurality of positionslocated proximate the sputter track region are included in the adjustedset of flaw data points which is counted to determine the flaw count andthe cleanliness factor.

[0034] In accordance with another such method, the members of theadjusted set of flaw data points is classified according to ranges ofsize to form a plurality of subsets of size-classified flaw data points,where “n” represents the number of sets in the plurality of subsets ofsize-classified flaw data points. The members of each of the pluralityof subsets of size classified flaw data points are counted to determineflaw counts C_(F1), . . . C_(Fn). Then, a plurality of cleanlinessfactors F_(Cj)=(C_(Fj)/C_(DP))×10⁶ are calculated to characterize thesputter target material.

[0035] Preferred apparatus for non-destructive characterization of atest sample of a sputter target material comprises an ultrasonictransducer for irradiating the test sample with sonic energy anddetecting echoes induced by the sonic energy to generate RF electricamplitude signals; an X-Y scanner mounting the ultrasonic transducer forcontrolled movement of the ultrasonic transducer relative to the surfaceof the test sample; a pre-amplifier for receiving and amplifying the RFelectric amplitude signals; a linear amplifier with a set of calibratedattenuators for generating modified amplitude signals related to the RFelectric amplitude signals; an analog-to-digital converter for receivingthe modified amplitude signals and generating digital signals related tosaid modified amplitude signals; and a microprocessor controller. Mostpreferably, the microprocessor controller includes a firstmicroprocessor and a second microprocessor. In accordance with anespecially preferred apparatus, the first microprocessor is programmedto regulate the controlled movement of the ultrasonic transducerrelative to the surface; to receive the digital signals, and to transferthe digital signals to the second microprocessor. The secondmicroprocessor is programmed to compare the modified amplitude signalswith the one or more calibration values to detect flaw data points atcertain where comparison of the modified amplitude signals with the oneor more calibration values indicates at least one flaw; and to detectno-flaw data points at other positions where comparison of the modifiedamplitude signals with the at least one calibration value indicates noflaw. In addition, the second microprocessor is programmed to bindgroups of the flaw data points corresponding to single large flaws so asto generate an adjusted set of flaw data points in which each group ofthe groups of flaw data points is replaced with a single, mostsignificant data point; to count members of the adjusted set of flawdata points to determine at least one flaw count C_(F); to determine atotal number of data points C_(DP); and to calculate a cleanlinessfactor F_(C)=(C_(F)/C_(DP))×10⁶.

[0036] Unlike the prior art method described earlier, the method of thepresent invention provides a characterization of the target cleanlinessfor different sputtering-affected regions of the target separately, thatmakes it possible to establish different cleanliness specification fordifferent sputter regions and to improve the target production yield.

[0037] Furthermore, unlike the prior art method, the method of thepresent invention provides a characterization of the cleanliness ofsputter target material for sputter track region separately by countingthe flaw at least for three size ranges such as the size range which isless or equal to 0.5 mm, the size range which is of 0.5 mm to 0.8 mm,and the size range which is equal to or larger 0.8 mm.

[0038] Most preferably, the test sample is compressed along onedimension, such as by rolling or forging, and then irradiated by sonicenergy propagating transversely, that is, obliquely or, better yet,normally, to that dimension. This has the effect of flattening andwidening any flaws in the material. The widening of the flaws, in turn,increases the intensity of the sonic energy scattered by the flaws andreduces the likelihood that the sonic energy will refract around theflaws.

[0039] These methods for characterizing sputter target materials may beused in processes for manufacturing sputter targets. As noted earlier,the cleanliness of a sputter target is one factor determining thequality of the layers or films produced by the target. By processingonly those sputter target blanks having cleanliness characteristicsmeeting certain reference criteria for sputter track and non-sputtertrack regions and rejecting blanks not meeting those criteria, oneimproves the likelihood that the sputter targets so manufactured willproduce high quality layers or films at the same time the manufacturer'sproduction yield can be increased reducing the cost of production andincreasing production throughput.

[0040] Therefore, it is one object of the invention to provide animproved non-destructive methods for characterizing sputter targetmaterials. Other objects of the invention will be apparent from thefollowing description, the accompanying drawings, and the appendedclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0041]FIG. 1 is a schematic view illustrating a first prior art methodof ultrasonic texture analysis;

[0042]FIG. 2 is a schematic view illustrating a second prior art methodof ultrasonic texture analysis;

[0043]FIG. 3 is a schematic view of an apparatus for carrying out themethod of FIG. 2;

[0044]FIG. 4 is a schematic view illustrating an especially-preferredmethod of ultrasonic cleanliness characterization in accordance with theinvention;

[0045]FIG. 5 is a schematic view of an apparatus for carrying out themethod of FIG. 4;

[0046]FIG. 6 is a histogram characterizing a relatively “clean”Al-0.2Si-0.5 wt % Cu material in accordance with an especially-preferredform of the invention; and

[0047]FIG. 7 is a histogram characterizing a less “clean” Al-0.2Si-0.5wt % Cu material in accordance with the especially-preferred form of theinvention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0048]FIG. 4 illustrates an especially-preferred method forcharacterizing the cleanliness of sputter target material. In accordancewith this method, a cylindrical sample 50′ of the sputter targetmaterial (which preferably comprises metal or a metal alloy) iscompressed or worked to produce a disc-shaped test sample 52′ having aplanar upper surface 54′ and a substantially planar lower surface 56′approximately parallel to the upper surface 54′. Thereafter, a focusedultrasonic transducer 60′ is positioned near the upper surface 54′. Thetransducer 60′ irradiates the entire upper surface 54′ of the testsample 52′, or a region of the upper surface 54′ less than the entiresurface, with single, short-duration, megahertz-frequency-rangeultrasonic pulses 62′. The transducer 60′ subsequently detects echoes64′ induced in the test sample 52′ by the pulses 62′. The transducer 60′converts the echoes 64′ into electrical signals (not shown), which areprocessed for use in characterizing the test sample 52′.

[0049] More specifically, the sample 50′ first is compressed along adimension 70′ to form the disc-shaped test sample 52′. Preferably, thesample 50′ is compressed by forging or rolling of the sample 50′,followed by diamond cutting to prepare the planar surfaces 54′ and 56′.The compression of the sample 50′ flattens and widens any flaws 72′, soas to increase the surface area of the flaws 72′ normal to the dimension70′.

[0050] As illustrated in FIG. 5, the test sample 52′ then is immersed indeionized water (not shown) in a conventional immersion tank 80′. Thetransducer 60′ is mounted on a mechanical X-Y scanner 82′ in electricalcommunication with a controller 84′ such as a PC controller. Thecontroller 84′ is programmed in a conventional manner to induce themechanical X-Y scanning unit 82′ to move the transducer 60′ in araster-like stepwise motion across the upper surface 54′ of the testsample 52′.

[0051] The presently preferred transducer 60′ is sold by ULTRAN USAunder the designation WS50-10-P4.5. This is a high resolutionpiezoelectric transducer having a fixed focalization distance. At a peakfrequency of approximately 10 MHz with an 8 MHz (−6 dB) bandwidth, thetransducer produces a pulse 62′ having a focal distance of approximately115 mm and a focal spot of approximately 0.6 mm in diameter.

[0052] Most preferably, the upper surface 54′ of the sample 52′ has awidth or diameter on the order of approximately 13 inches (approximately33 cm). Data acquisition steps of approximately 0.8 mm in both theX-direction and the Y-direction permit the detection of flaws equivalentto a 0.25 mm blind flat-bottom hole located at the back wall of a 25 mmthick reference standard at a detection level of −6 dB without exposurearea overlap. One thereby irradiates approximately 140,000 test pointson the upper surface 54′.

[0053] It is within the contemplation of the invention to irradiate onlya region of the upper surface 54′ of the sample 52′, such as the sputtertrack region (not shown). More specifically, it is within thecontemplation of the invention to program the microprocessor controller84′ so as to induce the X-Y scanning unit 82′ to scan only points in ornear a region of interest (not shown) less than the entire upper surface54′. The preparation of a suitable program for accomplishing this iswithin the ordinary skill in the art and requires no undueexperimentation.

[0054] Most preferably, the transducer 60′ is oriented so that the pulse62′ propagates through the deionized water (not shown) in the immersiontank 80′ and strikes the test sample 52′ approximately normally to theupper surface 54′. Furthermore, the transducer 60′ is preferably spacedfrom the upper surface 54′ such that the pulse 62′ is focused on a zone86′ (FIG. 4) of the test sample 52′. The preferred zone 86′ isapproximately 25 mm thick and is separated from the upper surface 54′ byan interval 55′ of approximately 4.5 mm. The pulse 62′ interacts withthe sample 52′ to induce echoes 64′, which then propagate back throughthe deionized water (not shown) to the transducer 60′ approximately 60μsec after the pulse is sent.

[0055] An especially-preferred echo acquisition system includes a lownoise gated preamplifier 90′; a low noise linear amplifier 92′ with aset of calibrated attenuators (not shown) having a signal-to-noise(texture) ratio which identifies definitely a signal equivalent to thatfrom a blind, flat-bottom hole of 0.25 mm in diameter located 25 mmbelow upper surface 54′; a 12-bit (2.44 mV/bit) analog-to-digitalconverter 94′; and, optionally, a digital oscilloscope 95′. Whensufficient time has elapsed for the echoes to arrive at the transducer60′, the controller 84′ switches the transducer 60′ from a transmittingmode to a gated electronic receiving mode. The echoes 64′ are receivedby the transducer 60′ and converted into an RF electric amplitude signal(not shown). The amplitude signal is amplified by the preamplifier 90′and then filtered by the low noise linear amplifier 92′ to produce amodified amplitude signal. The modified amplitude signal then isdigitized by the analog-to-digital converter 94′ before moving on to thecontroller 84′. The analog-to-digital conversion is performed so as topreserve amplitude information from the analog modified amplitudesignal.

[0056] The especially-preferred PC controller 84′ includes a firstmicroprocessor 100′ and a second microprocessor 110′. The firstmicroprocessor 100′ is programmed to control the movement of thetransducer 60′ and the data acquisition process. An especially-preferredsoftware package used in connection with the data acquisition system isavailable from Structural Diagnostics, Inc. under the designationSDI-5311 Winscan 4.

[0057] To improve the overall productivity of the system, the acquiredraw data information is transferred in the form of a raw data file (notshown) to the second microprocessor 110′. The division of dataacquisition and data processing tasks between the first and secondmicroprocessors 100′, 110′ facilitates the performance of uninterrupted,continuous and simultaneous data acquisition and data processing for agroup of samples (not shown), allowing two of the samples (not shown) tobe processed simultaneously. While the sample 52′ is subjected to thedata acquisition process controlled by the first microprocessor 100′,another sample (not shown) with previously acquired data is analyzed forcleanliness by the second microprocessor 110′. This division of laborimproves the overall test productivity when a queue of samples (notshown) can be expected.

[0058] The preferred samples 52′ take the form of flat discs which areinscribed into the X-Y raster-type scanning envelope. As a consequence,the second microprocessor 110′ extracts only target-related data pointsfrom the raw data. That is, the second microprocessor 110′ is programmedto “crop” the data so that only data points taken from locationsinternal to the surface 54′ are processed and boundary data points areexcluded.

[0059] The second microprocessor 110′ also is programmed to calculateone or more cleanliness factors characterizing the material of thesamples 50′ (FIG. 4), 52′. More precisely, it is programmed todiscriminate texture-related backscattering noise so as to distinguishflaw-related data points, that is, data points where comparison of thedigitized, modified amplitude signals with the reference valuesindicates the presence of flaws. It maintains a count of the flaw datapoints detected during the testing of a test sample 52′, eitherthroughout the entire upper surface 54′ (FIG. 4) or within a particularregion of interest such as the sputter track region (not shown), todetermine one or more “flaw counts” C_(F). The second microprocessor110′ also is programmed to distinguish “no-flaw data points,” that is,data points or pixels where comparison of the digitized, modifiedamplitude signals with the calibration values indicates the absence offlaws.

[0060] The second microprocessor 110′ also is programmed to calculatethe total number of flaws per the sample falling into three distinctsize ranges with flaw sizes equal to, or smaller than, 0.5 mm; with flawsizes in the range of 0.5 mm to 0.8 mm; and with flaw sizes equal to, orlarger than, 0.8 mm.

[0061] The second microprocessor 110′ also is programmed to analyze theplurality of data points (pixels) in respect to their physical locationoutside or inside of the sputter track region (not shown) or anotherregion of interest (not shown). One means for accomplishing this is toassociate each digitized, modified amplitude signal with the location(not shown) of the transducer 60′ in the X-Y raster grid (e.g., by meansof the order in which the data points are arranged in the raw data file(not shown) transferred from the first microprocessor 100′) where theecho 64′ which produced that digitized, modified amplitude signal wasinduced; and to compare that location (not shown) with the position ofthe sputter track region (not shown) or other region of interest (notshown) on the upper surface 54′ of the sample 52′. In accordance with aparticularly preferred method, the second microprocessor 110′ isprogrammed to determine the number of flaws located inside the sputtertrack region (not shown) which fall into the three distinct size ranges,namely, with flaw sizes equal to, or smaller than, 0.5 mm; with flawsizes in the range of 0.5 mm to 0.8 mm; and with flaw sizes equal to, orlarger than, 0.8 mm.

[0062] The second microprocessor 110′ also is programmed to analyze aplurality of data points or pixels (not shown) in the way which bindsgroups of data points representing a single flaw (not shown) of a sizelarger than that represented by a single data point or pixel. In otherwords, when the second microprocessor 110′ detects a flaw data pointhaving a digitized, modified amplitude value greater than a thresholdvalue, it is programmed to examine the data points associated withlocations on the upper surface 54′ of the sample 52′ immediatelysurrounding the location from which that data point was derived. If thesecond microprocessor 10′ finds a cluster or group of flaw data pointsderived from adjacent locations on the upper surface 54′, it extractsthe flaw data point having the largest digitized, modified amplitudevalue from the group. The second microprocessor 110′ then replaces thegroup with that single, most significant data point and determines thesize of the flaw based on a comparison of the digitized, modifiedamplitude value associated with that single, most significant data pointwith the calibration values.

[0063] Most preferably, the second microprocessor 11O′ will replace eachsuch group of data points with a single, most significant data point toform an adjusted set of flaw data points before it classifies the flawdata points by size or location relative to the upper surface 54′. Thisis so that the second microprocessor 110′ can detect any clusters orgroups of flaw data points derived from adjacent locations on the uppersurface 54′ before the order of the data points in the raw data file(not shown) is deranged by the process of categorizing the flaw datapoints.

[0064] It is known that different types of flaws 72′ can have differenteffects on the waveform characteristics, such as phase, of the echoes64′. Thus, in some applications, it may be desirable to include thedigital oscilloscope 95′ or comparable means to monitor the digitized,modified amplitude signals for waveform phase inversion and to comparethe digitized, modified amplitude signals obtained from the sample 52′with the calibration values. In particular, information concerning thephysical characteristics of the flaws 72′ can be derived from comparisonof the digitized, modified amplitude signals with calibration valuesderived from tests conducted on reference samples (not shown) havingcompositions similar to those of the test sample 50′ (FIG. 4), 52′ anddifferent types of artificially-created flaws (not shown). Examples ofsuch artificially-created flaws (not shown) include blind, flat-bottomholes of fixed depth and diameter (not shown); and refractory particlesof given size (not shown) artificially embedded into reference samplesmaterial (not shown), such as alumina particles in an aluminum oraluminum alloy target. Although such analysis provides additionalinformation regarding the cleanliness of a material sample, thoseskilled in the art will recognize that such analysis is not critical tothe present invention.

[0065] The second microprocessor 110′ also determines a total number ofdata points “C_(DP)” in the region of the upper surface 54′ scanned bythe transducer 60′ which yield information regarding the presence orabsence of flaws; in other words, “C_(DP)” equals the sum of the flawcount C_(F) (or of a series of flaw counts “C_(F1),” . . . “C_(Fn)”representing numbers of flaws classified according to flaw size,position relative to a region of interest (not shown) of the uppersurface 54′ or the like) and the number of no-flaw data points. AlthoughC_(DP) could be determined by adding counts of the flaw data points andthe no-flaw data points, it is preferably determined by counting thetotal number of positions “C₁” along the region of interest (not shown)of the upper surface 54′ at which the sample 52′ is irradiated by thetransducer 60′ and subtracting the number of digitized signals “C_(N)”which the data acquisition circuitry was unable, due to noise or othercauses, to identify as either flaw data points or no-flaw data points.

[0066] Having determined the flaw count C_(F)(or multiple flaw counts“C_(F1),” . . . “C_(Fn)”) and the total number of data points “C_(DP),”the second microprocessor 110′ is programmed to calculate one or morecleanliness factors F_(Cm)=(C_(Fm)/C_(DP))×10⁶ to characterize thematerial comprising the samples 50′ (FIG. 4), 52′. The preparation of asuitable program for determining these one or more cleanliness factorsin accordance with the invention as disclosed herein is within theordinary skill in the art and requires no undue experimentation.

[0067] Another way in which to characterize the material comprising thesamples 50′ (FIG. 4), 52′ is by determining the size distribution offlaws in the test sample 52′. More specifically, one may characterizethe cleanliness of the sample 52′ by defining amplitude bands or ranges;comparing the amplitudes of the digitized, modified amplitude signalswith the amplitude bands to form subsets of the modified amplitudesignals; counting these subsets of modified amplitude signals todetermine a modified amplitude signal count for each amplitude band; andconstructing a histogram (not shown) relating the modified signal countsto said plurality of amplitude bands. Since the amplitudes representedby the digitized modified amplitude signals are related to the sizes offlaws detected in the sample 52′, the histogram (not shown) provides anindication of the flaw size distribution in the sample 52′.

[0068] Turning now to FIGS. 6 and 7, there may be seen histograms 120(FIG. 6) and 122 (FIG. 7) characterizing two Al/0.2 wt % Si/0.5 wt % Cualloy sputter target materials (not shown) having orthorhombic texturesand grain sizes in the range of 0.08 mm to 0.12 mm. Each material wasdeformed into a disc-shaped sample having a surface area ofapproximately 78.4 in² (506 cm²) and irradiated at approximately 6.4×10⁴positions. The material of FIG. 6 was “cleaner” (F_(C)≈47) than that ofFIG. 7 (F_(C)≈125). The thickness zone of flaw monitoring was locatedwithin a gate of 4.5 microsecond duration with a gate delay of 1.5microseconds.

[0069] The abscissae 130 (FIG. 6), 132 (FIG. 7) of the histograms ofFIGS. 6 and 7 represent amplitude normalized as a percentage of the echoamplitude induced in a reference sample (not shown) having a 0.8 mmblind, flat-bottom hole. The ordinates 140 (FIG. 6), 142 (FIG. 7) inFIGS. 6 and 7 represent the modified signal counts for each amplitude,expressed on a logarithmic scale. The echo amplitude thresholds for theflaw counts were set to 15.9% since, as established experimentally, thetexture-related echo amplitude did not exceed 15% for all aluminumalloys tested. The preparation of a suitable program for plottinghistograms such as the histograms 120 (FIG. 6), 122 (FIG. 7) shown inFIGS. 6 and 7 in accordance with the invention as disclosed herein iswithin the ordinary skill in the art and requires no undueexperimentation.

[0070] From the foregoing, it will be apparent that one object of thepresent invention is to provide a non-destructive method forcharacterizing the cleanliness of a sputter target material which iscapable of distinguishing the sizes and locations of flaws detected inthe material. It will be apparent that another object of the presentinvention is to provide a method capable of distinguishing single,relatively large flaws from a plurality of smaller, closely spacedflaws. It will be apparent that another advantage of the invention is toprovide a relatively efficient method capable of performing simultaneousdata acquisition and data processing on different material samples in aqueue.

[0071] While the method and form of apparatus herein describedconstitutes a preferred embodiment of this invention, it is to beunderstood that the invention is not limited to this precise method andform of apparatus, and that changes may be made therein withoutdeparting from the scope of the invention which is defined in theappended claims.

What is claimed is:
 1. A non-destructive method for characterizing atest sample of a sputter target material defining a surface, said methodusing one or more calibration values and comprising the steps of: a)sequentially irradiating the test sample with sonic energy at aplurality of positions on the surface; b) detecting echoes induced bythe sonic energy; c) discriminating texture-related backscattering noisefrom the echoes to obtain modified amplitude signals; d) comparing themodified amplitude signals with the one or more calibration values todetect i) flaw data points at certain positions of the plurality ofpositions where comparison of the modified amplitude signals with theone or more calibration values indicates at least one flaw, and ii)no-flaw data points at other positions of the plurality of positionswhere comparison of the modified amplitude signals with the at least onecalibration value indicates no flaw; e) binding groups of the flaw datapoints corresponding to single large flaws so as to generate an adjustedset of flaw data points in which each group of the groups of flaw datapoints is replaced with a single, most significant data point; f)counting members of the adjusted set of flaw data points to determine atleast one flaw count C_(F); g) determining a total number of data pointsC_(DP); and h) calculating a cleanliness factorF_(C)=(C_(F)/C_(DP))×10⁶.
 2. The method as recited in claim 1 whereinthe surface defines a sputter track region; each flaw data point isassociated with one of the plurality of positions; and substantially allof the members of the adjusted set of flaw data points are associatedwith positions of the plurality of positions located proximate thesputter track region.
 3. The method as recited in claim 1 including theadditional step of classifying the members of the adjusted set of flawdata points according to ranges of size to form a plurality of subsetsof size-classified flaw data points, where “n” represents the number ofsets in the plurality of subsets of size-classified flaw data points;said step f) includes counting members of the plurality of subsets ofsize classified flaw data points to determine flaw counts C_(F1), . . .C_(Fn); and said step h) includes calculating a plurality of cleanlinessfactors F_(Cj)=(C_(Fj)/C_(DP))×10⁶.
 4. The method as recited in claim 1including the additional step of classifying the members of the adjustedset of flaw data points into a first subset of flaw data pointsrepresenting flaws of flaw size equal or smaller than 0.5 mm, a secondsubset of flaw data points representing flaws of flaw size in the rangeof 0.5 mm to 0.8 mm, and a third subset of flaw data points representingflaws of flaw size equal or larger than 0.8 mm; said step f) includescounting members of the first subset of flaw data points to determine afirst flaw count C_(F1); said step f) further includes counting membersof the second subset of flaw data points to determine a second flawcount C_(F2); said step f) further includes counting members of thethird subset of flaw data points to determine a third flaw count C_(F3);said step h) includes calculating a first cleanliness factorF_(C1)=(C_(F1)/C_(DP))×10⁶; said step h) further includes calculating asecond cleanliness factor F_(C2)=(C_(F2)/C_(DP))×10⁶; and said step h)further includes calculating a third cleanliness factorF_(C3)=(C_(F3)/C_(DP))×10⁶.
 5. The method as recited in claim 1including the additional step of determining the calibration values byirradiating a reference sample comprising blind, flat-bottomed, holes offixed depth and diameter; detecting at least one echo induced by thesonic energy; and discriminating texture-related backscattering noisefrom the at least one echo to obtain the one or more calibration values.6. The method as recited in claim 1 including the additional step ofdetermining the calibration values by irradiating a reference samplecomprising alumina particles of given size artificially embedded;detecting at least one echo induced by the sonic energy; anddiscriminating texture-related backscattering noise from the at leastone echo to obtain the one or more calibration values.
 7. The method asrecited in claim 1 including the additional step of deforming acylindrical sample to disc shape to form said test sample prior to saidstep a).
 8. A non-destructive method for characterizing a test sample ofa sputter target material defining a surface, said method using one ormore calibration values and comprising the steps of: a) sequentiallyirradiating the test sample with sonic energy at a plurality ofpositions on the surface; b) detecting echoes induced by the sonicenergy; c) discriminating texture-related backscattering noise from theechoes to obtain modified amplitude signals; d) comparing the modifiedamplitude signals with the one or more calibration values to detect i)flaw data points at certain positions of said plurality of positionswhere comparison of the modified amplitude signals with the one or morecalibration values indicates at least one flaw, and ii) no-flaw datapoints at other positions of said plurality of positions wherecomparison of the modified amplitude signals with the at least onecalibration value indicates no flaw; e) binding groups of said flaw datapoints corresponding to single large flaws so as to generate an adjustedset of flaw data points in which each group of the groups of flaw datapoints is replaced with a single, most significant data point; f)defining a plurality of amplitude bands; g) comparing the amplitudesrepresented by members of the adjusted set of flaw data points with theamplitude bands to form a plurality of subsets of the adjusted set offlaw data points, where “n” is the number of the subsets of the adjustedset of flaw data points; h) counting members of the subsets of theadjusted set of flaw data points to determine a plurality of flaw countsC_(F1), . . . C_(Fn); and i) constructing a histogram relating said flawcounts C_(F1), . . . C_(Fn) to said amplitude bands.
 9. The method asrecited in claim 8 wherein the surface defines a sputter track region;each flaw data point is associated with one of the plurality ofpositions; and substantially all of the members of the adjusted set offlaw data points are associated with positions of the plurality ofpositions located proximate the sputter track region.
 10. The method asrecited in claim 8 including the additional step of determining thecalibration values by irradiating a reference sample comprising blind,flat-bottomed, holes of fixed depth and diameter; detecting at least oneecho induced by the sonic energy; and discriminating texture-relatedbackscattering noise from the at least one echo to obtain the one ormore calibration values.
 11. The method as recited in claim 8 includingthe additional step of determining the calibration values by irradiatinga reference sample comprising alumina particles of given sizeartificially embedded; detecting at least one echo induced by the sonicenergy; and discriminating texture-related backscattering noise from theat least one echo to obtain the one or more calibration values.
 12. Themethod as recited in claim 8 including the additional step of deforminga cylindrical sample to disc shape to form said test sample prior tosaid step a).
 13. (Original)Apparatus for non-destructivecharacterization of a test sample of a sputter target material defininga surface, said apparatus comprising: a) an ultrasonic transducer forirradiating the test sample with sonic energy and detecting echoesinduced by the sonic energy to generate RF electric amplitude signals;b) an X-Y scanner mounting said ultrasonic transducer for controlledmovement of said ultrasonic transducer relative to said surface; c) apre-amplifier for receiving and amplifying said RF electric amplitudesignals; d) a linear amplifier with a set of calibrated attenuators forgenerating modified amplitude signals related to said RF electricamplitude signals; e) an analog-to-digital converter for receiving saidmodified amplitude signals and generating digital signals related tosaid modified amplitude signals; and f) a microprocessor controllerprogrammed to regulate the controlled movement of said ultrasonictransducer relative to the surface, to receive said digital signals, tocompare the modified amplitude signals with the one or more calibrationvalues to detect i) flaw data points at certain positions of saidplurality of positions where comparison of the modified amplitudesignals with the one or more calibration values indicates at least oneflaw and ii) no-flaw data points at other positions of said plurality ofpositions where comparison of the modified amplitude signals with the atleast one calibration value indicates no flaw, to bind groups of saidflaw data points corresponding to single large flaws so as to generatean adjusted set of flaw data points in which each group of the groups offlaw data points is replaced with a single, most significant data point,to count members of the adjusted set of flaw data points to determine atleast one flaw count C_(F), to determine a total number of data pointsC_(DP), and to calculate a cleanliness factor F_(C)=(C_(F)/C_(DP))×10⁶.14. The apparatus as recited in claim 13 wherein said microprocessorcontroller includes a first microprocessor and a second microprocessor.15. The apparatus as recited in claim 13 wherein said microprocessorcontroller includes a first microprocessor and a second microprocessor;said first microprocessor being programmed to regulate the controlledmovement of said ultrasonic transducer relative to the surface, toreceive said digital signals, and to transfer said digital signals tosaid second microprocessor; and said second microprocessor beingprogrammed to compare the modified amplitude signals with the one ormore calibration values to detect i) flaw data points at certainpositions of said plurality of positions where comparison of themodified amplitude signals with the one or more calibration valuesindicates at least one flaw and ii) no-flaw data points at otherpositions of said plurality of positions where comparison of themodified amplitude signals with the at least one calibration valueindicates no flaw, to bind groups of said flaw data points correspondingto single large flaws so as to generate an adjusted set of flaw datapoints in which each group of the groups of flaw data points is replacedwith a single, most significant data point, to count members of theadjusted set of flaw data points to determine at least one flaw countC_(F), to determine a total number of data points C_(DP), and tocalculate a cleanliness factor F_(C)=(C_(F)/C_(DP))×10⁶.